Sunday, March 18, 2012

A Nonergodic Model of the Business Cycle

Figure 1: A Fractal in the Phase Diagram for One Specification of Parameters in the Kaldor Model1

1.0 Introduction

I thought I would try to combine an ability for computers to draw
fractals
with an economic model that suggests practical conclusions. In this post, I
merely duplicate some results in the literature. In a deterministic
ergodic process, as I understand it, all trajectories pass through every state
in whatever attractor may exist. Hence, the Kaldor model, like
some
dynamical
systems arising in mathematics, is non-ergodic.

2.0 The Model

In 1940, Nicholas Kaldor proposed a model of the business cycle.
It can be expressed by four equations2.
National ouput evolves from the previous period as a response to
aggregate demand:

Yt+1 = Yt + α(It - St),

where Yt is the value of output in year t,
It is intended investment, and
St is intended saving.
The parameter α represents the speed of adjustment to
excess aggregate demand. The evolution of the value of the capital
stock depends on investment and depreciation:

Kt+1 = It + (1 - δ)Kt,

where δ is the depreciation rate of capital stock.
Intended saving is directly proportional to output:

St = σYt,

where σ is the (average and marginal) propensity to save.
An investment function3 is the final equation specifying the model:

It = σμ + γ(σμ/δ - Kt) + Tan-1(Yt - μ),

where μ is the expected level of output, and
γ represents the costs of adjusting the capital stock.
Along with some restrictions on the values of parameters, the
model is now fully specified. The arc tangent function provides a
s-shaped non-linear term, such that entrepreneurs increase
investment when output exceeds their expectations4.

I find it convenient to
define new variables normalized around a stationary state:

kt = Kt - σμ/δ

yt = Yt - μ

The model, expressed in terms of normalized capital stocks and normalized output, is:

kt + 1 = Tan-1(yt) + (1 - δ - γ)kt

yt + 1 = (1 - ασ)yt + αTan-1(yt) - αγkt

Note that the following is a solution:

For all time t,
yt = kt = 0.

3.0 Some Results

The above version of the Kaldor model is a discrete-time dynamical system, defined by a map from the two-dimensional real plane (k, y) to the same space. Four5 parameters are used to define the map. Questions for the mathematician revolve around describing how the phase portrait for the system varies qualitatively with variations in the parameters. Complex and chaotic behavior can arise in the Kaldor model with appropriate choices of parameter values.

For a small enough speed of adjustment and large enough propensity to save, the dynamics is boring. All trajectories converge to the origin.

As the propensity to save decreases, the system goes through a pitchfork bifurcation, so-called because the bifurcation diagram looks like a pitchfork. The origin loses its stability, and two symmetric fixed points appear. For a small enough speed of adjustment, at least, the two symmetric fixed points exhibit local asymptotic stability. The location of these new fixed points must be found numerically. A fortiori, the computer must be used as an aid to perform a local stability analysis of these points, based on the eigenvalues of the Jacobian matrix.

As the speed of adjustment increases, the boundary between the basins of attraction becomes more complex. Figure 1 shows a case where they are entangled in a fractal-like structure, and the outer perimeter of the colored area is repelling. The limit cycle shown is a short distance outside this repelling boundary.

I have by no means exhausted the dynamics of the Kaldor model. Consider a region in which the origin is the only fixed point, and it is asymptotically stable. As the speed of adjustment increases, the system undergoes a Neimark-Sacker bifurcation, which, I gather, is the discrete-time analog to a Hopf bifurcation. Cycles exist in which the cycle is not a fixed point on the Poincaré return map, but winds around many times before repeating. And if I want my application to explore all these dynamics, I have quite a bit of programming to do. I am curious if I will be able to plot a bifurcation diagram, given that the behavior at the limit depends on the initial value.

4.0 Observations

For the parameter values illustrated in the figures, trajectories have three possible destinations:

A stable equilibrium with lots of capital and high output.

Another stable equilibrium with less capital and less output.

A business cycle.

Furthermore, the boundary between the basins of attraction for the stable
limit points is fractal-like. These properties suggest that a random shock
to the system can redirect trajectories to a very different final
destination.

Although not illustrated above, the model exhibits structural instability.
A perturbation of the model parameters can result in different observable
behavior, of greater or less complexity.

One general way of conceptualizing business cycles is to see them as the
response of a damped linear system to exogenous shocks. Their height
and depth depends on the characteristics of the external impulses driving
the system. The Kaldor model suggests another possibility. In this model,
the properties and extent of business cycles are endogenously determined.
Shocks can drive the system from one trajectory to another, but the
range of possible behaviors is determined from within the system. It is
my impression that the former way of understanding business cycles is
dominant among mainstream macroeconomists, while the latter is closer to
describing actually existing capitalist economies.

11 comments:

Anonymous
said...

Hmm, this looks more like the mathematical masturbation one associates with the modern neoclassical DSGE crowd. Its useless for understanding reality.What about some real thinking, like Minsky and Keynes showed us ?

I don't want to sound like a mainstream economist but you *possibly* sound like you simply didn't understand or couldn't be bothered to understand the maths here, and your comment is borne more of a knee-jerk reaction to maths in economics than healthy pessimism.

No, anon, it doesn't look anything like the models you describe. Indeed, if the four relations in the model were described, it would be readily understood to be very closely related to General Theory thinking.

This type of mathematical exploration does not derive the relationships from armchair theorizing that all people in society make all decisions according to some 19th century long-since falsified stereotype ~ rather, it explores how the system would work depending on how strongly or weakly and how rapidly or slowly the relationships in the model work.

From this, unlike the a priori mainstream modeling based on known false assumptions, one can indeed pursue actual scientific exploration to determine whether significant relations have been omitted, and for the relations in the model how strong and rapid the relations appear to be.

Its no accident that when Steve Keen pursues his research on Minsky's theory, he relies heavily on computer modeling of dynamic relations.

How do you make these non-ergodic models? Which program do you use to draw the solutions? I think this kind of approach may help to design growth models including business cycles, and forget narrow-minded "stable" or "explosive" models towards a more complex way of thinking.

Genaro, I have been writing my own Java programs. I do not have a general program for exploring dynamical systems. Rather, I have been writing custom programs. You probably can do something like this in Matlab, Octave, or Mathematica. Part of the point for me is to review Java.

In the General Theory, Keynes assumes that short run expectations, on the level of capacity utilization, are usually adjusted until they are met. But long run expectations, on the amount of capacity, are given. When long run expectations can be compared with results, it is too late to do anything about their failure. So, to expand on Bruce's point, Kaldor's assumption of a given expectation for income and the corresponding needed capital is an extremely simple way of building on Keynes, in my opinion. (I have previouslymentioned extending Keynes to the long period. Debates have arisen in the literature on whether these extension treat finance at all adequately.)

Here's an interesting explanation for growth theories from Vernengo. I think Kaldor's model does not fit in (C). Kaldor model implies that, when capitalists decide to invest, they don't ever think they won't sell the production. Expected demand has to be pro-cycle at least. They cannot be fixed, they have to tend towards a capital accumulation path. This does not mean that there is a long term equilibrium or full utilisation of resources, because the model tends to a normal utilisation level, and "equilibrium" relies on the exogenous demand growth rate, wich is always changing.